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Gather debugging folklore from more practitioners, each a verbatim quote
checked against a cached source copy (footnoted with line numbers):
- koaning (Vincent Warmerdam), "Bad Labels": benchmark labels are often wrong;
find them with confidence-sorted errors.
- gwern, the tank-detection legend: the canonical data-leakage parable, plus
the scout-mindset twist that it's a likely-unsourced urban legend.
- Patrick Kidger, "Just Know Stuff": why research code is buggy ("kludge ...
bugs that don't cripple things only because some other bug stops them") and
"never accept the kludge". Plus a one-line jaxtyping pointer for shape bugs.
- nanochat (Karpathy): BOS-alignment fake metric improvement; all-ranks must
clip on inf (a multi-GPU bug single-GPU testing hides).
- cleanrl "37 Implementation Details of PPO" -> RL sub-skill, as the canonical
proof that reference-impl details (not ideas) decide whether PPO works.
Trim the lucidrains item to one quote (it had ballooned). Add wassname credit
+ companion-gist link. All 20 footnotes resolve.
Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
10 lines
1.1 KiB
Markdown
10 lines
1.1 KiB
Markdown
# The Neural Net Tank Legend — Gwern Branwen
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Source: https://gwern.net/tank . Cached excerpt for the ML-debugging skill (verbatim abstract passages).
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> A cautionary tale in artificial intelligence tells about researchers training an neural network (NN) to detect tanks in photographs, succeeding, only to realize the photographs had been collected under specific conditions for tanks/non-tanks and the NN had learned something useless like time of day. This story is often told to warn about the limits of algorithms and importance of data collection to avoid "dataset bias"/"data leakage" where the collected data can be solved using algorithms that do not generalize to the true data distribution, but the tank story is usually never sourced.
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> I collate many extent versions dating back a quarter of a century to 1992 along with two NN-related anecdotes from the 1960s; their contradictions & details indicate a classic "urban legend", with a probable origin in a speculative question in the 1960s by Edward Fredkin at an AI conference about some early NN research, which was then classified & never followed up on.
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